基于卷积神经网络的"舌边白涎"舌象识别研究OACSTPCD

Objective To study the local feature recognition of tongue image through machine-learning analysis on the characteristics of"white saliva on the tongue side",and to explore the performance of convolutional neural network algorithms in identifying tongue images application.Methods Python was used for image preprocessing,and visual geometry group 16(VGG16)convolutional neural network model was built for tongue image recognition.The effect of the model on tongue image recognition of"white saliva on the tongue side"was identified and analyzed,and the typical tongue image performance of"white saliva on the tongue side"was analyzed combined with heat map.Results Based on PyTorch framework,tongue image identification research of convolutional neural network was carried out.The verification accuracy of VGG16 and residual network 50(ResNet50)models were high,reaching over 80%,and the ResNet50 model outperformed the VGG16 model,providing a certain reference for tongue image recognition.Based on gradient-weighted class activation mapping(Grad-CAM)technology,the network visualization of the difference distribution of tongue coating and tongue color was helpful for intuitive model evaluation and analysis.Conclusion The analysis of tongue image database based on convolutional neural network model can realize the tongue image recognition of"white saliva on the tongue side",which is helpful for objectified auxiliary analysis in clinical diagnosis and treatment,and provides some references for the intelligent development of tongue diagnosis.